{"id":"W3090461013","doi":"10.1109/ijcnn48605.2020.9207301","title":"Dynamic Network Link Prediction by Learning Effective Subgraphs using CNN-LSTM","year":2020,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Construct (python library); Link (geometry); Convolutional neural network; Dynamic network analysis; Heuristic; Process (computing); Machine learning; Set (abstract data type); Artificial neural network; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000245191,0.0010486,0.0005524299,0.001171059,0.000325682,0.0004994801,0.001157847,0.0006408783,0.00166246],"category_scores_gemma":[0.001342813,0.0004160001,0.0004988698,0.001039622,0.0003318558,0.002016984,0.0006092533,0.0009566848,0.0004920313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055897,"about_ca_system_score_gemma":0.0006718523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01807867,"about_ca_topic_score_gemma":0.03344416,"domain_scores_codex":[0.9998465,0.00001529359,0.000007481132,0.00006494512,0.00003244599,0.00003333967],"domain_scores_gemma":[0.9996815,0.0001014589,0.00006037926,0.000045817,0.00008189462,0.0000290334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001425601,0.0001457433,0.006543442,0.0001077136,0.00008980147,0.0002083885,0.00007433056,0.7087034,0.006522042,0.005094964,0.006888225,0.2654794],"study_design_scores_gemma":[0.000001948767,0.000007758439,0.0002675633,0.000003847517,0.000005908527,0.00001196433,0.000007430713,0.9965918,0.0007346994,0.002138133,0.0002266246,0.000002308285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2602897,0.001473429,0.7227622,0.0007350669,0.0001913844,0.0001305185,0.001762525,0.005627653,0.007027503],"genre_scores_gemma":[0.9148142,0.0004417733,0.07830288,0.0001595666,0.00005622804,0.00007818792,0.00230722,0.0001272111,0.003712497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01807867,"threshold_uncertainty_score":0.03594691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006617371965421524,"score_gpt":0.2379252819676664,"score_spread":0.2313079100022448,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}